基于压缩感知(Compressed Sensing, CS)的信道估计可以达到减少导频的目的，但在频-时域信道矩阵到时延-多普勒域的稀疏变换中存在谱泄漏现象，影响了信道矩阵的稀疏性和估计的均方误差(MSE)性能。为此该文对信道的稀疏性进行研究，提出一种时域加窗的稀疏优化CS信道估计算法。通过对时域加窗，所提算法抑制了由离散截断导致的多普勒域泄漏，再据此设计出观测矩阵，以此方式增强信道在时延-多普勒域的稀疏性，并实现对稀疏的信道矩阵更为准确的重构，达到改善信道估计MSE性能的目的。仿真结果表明随信噪比的增大，加窗CS算法相比无窗CS算法有效改善了信道估计的性能。
Channel estimation which based on Compressed Sensing (CS) can achieve the purpose of reducing pilots, but in the transformation of channel matrix from frequency-time domain to delay-Doppler sparse domain exists spectral leakage phenomenon which affects the sparsity of the channel and the Mean Squared Error (MSE) performance of estimation. For this, this paper studies the sparsity of the channel and a compressed channel estimation algorithm which optimized the sparsity by time domain windowing is proposed. With time domain windowing, the proposed algorithm restrains the leakage of Doppler domain which is caused by discretization and truncation, then the measurement matrix is designed. By this method, the sparsity of the delay-Doppler domain channel is enhanced and the more accurate sparse channel matrix is reconstructed. The channel estimation performance is improved. Simulation results show that with the signal-to-noise ratio increasing, windowed CS algorithm improves effectively the performance of channel estimation compared with no windows CS algorithm.